Generation of autonomy map for autonomous vehicle

ABSTRACT

Generation of an autonomy map for assisting an autonomous vehicle includes extracting the historical autonomy information associated one or more route segments of one or more routes. An autonomy level for each route segment is determined based on the extracted historical autonomy information. A digital autonomy map is generated including each route segment of at least one route such that each route segment is tagged with the determined autonomy level. The autonomy level of each route segment is dynamically updated in real-time for providing real-time driving assistance to the autonomous vehicle.

CROSS-RELATED APPLICATIONS

This application claims priority of Indian Application Serial No.201941018673, filed May 9, 2019, the contents of which are incorporatedherein by reference.

FIELD

Various embodiments of the disclosure relate generally todriving-assistance systems. More specifically, various embodiments ofthe disclosure relate to generation of an autonomy map for an autonomousvehicle.

BACKGROUND

With the advancement of technologies and demand of vehicles for modernday travels, autonomous vehicles are emerging rapidly in thetransportation industry. An autonomous vehicle can drive safely on itsown without human intervention during a part of a journey or the entirejourney. The autonomous vehicle includes components, such as sensors,actuators, smart computing devices, and communication devices, thatenable automatic generation of control signals for self-driving andnavigating along various routes. These components interact with eachother and collect and generate various control signals in real time toprovide an advance driver-assistance system (ADAS) to the autonomousvehicle or a driver of the autonomous vehicle in the driving processthat enhances the overall safety.

Generally, the autonomous vehicles operate in accordance with variouslevels of autonomy. According to the Society of Automotive Engineers(SAE) standards, the autonomous vehicles are provided with six levels ofautonomy. The six levels range from level “0” through level “5”. Here,level “0” corresponds to “no automated assistance”, and requires thedriver to control various driving-related functionalities of theautonomous vehicle. Level “5” corresponds to “full automation”, and doesnot require any intervention from the driver. The autonomous vehicle mayswitch through the autonomy levels while transiting from one location toanother location. Thus, the computation of the autonomy levels is verysignificant in order to ensure the safety of the autonomous vehicle aswell as the driver and passengers travelling in the autonomous vehicle,along with other objects that are in the surrounding of the autonomousvehicle.

For efficient computation of the autonomy levels, various internal andexternal factors must be taken into consideration. Further, the drivershould be well aware of the switching between the autonomy levels wellin advance. Instantaneous switching between the autonomy levels withoutprior notification to the driver or the autonomous vehicle maycompromise the overall safety that is not desirable. Further, deployingthe autonomous vehicle for a trip along with the driver is not alwaysfeasible considering the overall cost of the trip.

In light of the foregoing, there exists a need for a technical andreliable solution that takes into consideration the above-mentionedproblems, challenges, and short-comings, and facilitates effective andefficient driving assistance systems for autonomous vehicles.

SUMMARY

Generation of an autonomy map for an autonomous vehicle is providedsubstantially as shown in, and described in connection with, at leastone of the figures, as set forth more completely in the claims.

These and other features and advantages of the present disclosure may beappreciated from a review of the following detailed description of thepresent disclosure, along with the accompanying figures in which likereference numerals refer to like parts throughout.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram that illustrates an environment for generatinga digital autonomy map, in accordance with an exemplary embodiment ofthe disclosure;

FIG. 2 is a block diagram that illustrates a map server of theenvironment of FIG. 1, in accordance with an exemplary embodiment of thedisclosure;

FIG. 3 is a block diagram that illustrates a transportation server ofthe environment of FIG. 1, in accordance with an exemplary embodiment ofthe disclosure;

FIG. 4 is a block diagram that illustrates a database server of theenvironment of FIG. 1, in accordance with an exemplary embodiment of thedisclosure;

FIGS. 5A and 5B are diagrams that collectively illustrate the digitalautonomy map rendered on a navigation device of an autonomous vehicle ofthe environment of FIG. 1, in accordance with an exemplary embodiment ofthe disclosure;

FIGS. 6A and 6B are diagrams that collectively illustrate a navigationinterface rendered on the navigation device, in accordance with anexemplary embodiment of the disclosure;

FIGS. 7A and 7B, collectively, illustrate a flow chart of a method forgenerating a digital autonomy map for providing driving assistance tothe autonomous vehicle, in accordance with an exemplary embodiment ofthe disclosure; and

FIG. 8 is a block diagram that illustrates a system architecture of acomputer system for generating the digital autonomy map for theautonomous vehicle for providing real-time driving assistance to theautonomous vehicle, in accordance with an exemplary embodiment of thedisclosure.

DETAILED DESCRIPTION

Certain embodiments of the disclosure may be found in a disclosedapparatus for generating a digital autonomy map. Exemplary aspects ofthe disclosure provide a method and a system for generating the digitalautonomy map for an autonomous vehicle. The method includes one or moreoperations that are executed by circuitry of a map server to generatethe digital autonomy map. When a first autonomous vehicle is scheduledfor a ride between a source location and a destination location, thecircuitry may be configured to extract historical autonomy levelsassociated with one or more route segments of one or more routes of ageographical region. Each of the one or more routes may connect thesource location with the destination location. Prior to the extraction,the historical autonomy levels may be generated based on at leasthistorical driving conditions of the one or more route segments of theone or more routes and historical configuration of one or more sensorsand processing components of one or more second autonomous vehicles thathave traversed via the one or more routes in the past. The historicalautonomy levels may be extracted from a database server based on a timeor a time duration of a day associated with the first autonomousvehicle.

The circuitry may be further configured to generate the digital autonomymap for the first autonomous vehicle. The digital autonomy map mayinclude the one or more route segments of at least one route connectingthe source location with the destination location of the firstautonomous vehicle. Each route segment on the digital autonomy map maybe tagged with an autonomy level. The autonomy level may be determinedbased on the extracted historical autonomy levels. The circuitry may befurther configured to render the digital autonomy map on a display ofthe first autonomous vehicle. The digital autonomy map may be utilizedby the first autonomous vehicle for controlling and managing the transitoperations between the source location and the destination location. Forexample, the transit operations may be controlled and managed based onat least navigation and autonomy information indicated by the digitalautonomy map. The digital autonomy map generated for the firstautonomous vehicle may be different from another digital autonomy mapgenerated for another autonomous vehicle, when the first autonomousvehicle and another autonomous vehicle are associated with a differentvehicle category and different sensor configuration.

The circuitry may be further configured to receive real-time autonomylevels associated with the one or more route segments of the at leastone route from one or more third autonomous vehicles. The one or morethird autonomous vehicles may be currently traversing the one or moreroute segments of the at least one route. The one or more thirdautonomous vehicles may be traversing the one or more route segmentsahead of the first autonomous vehicle. The circuitry may be furtherconfigured to dynamically update the autonomy level of each routesegment of the at least one route on the digital autonomy map. Theautonomy level of each route segment may be dynamically updated based onat least the received real-time autonomy levels for providing real-timedriving assistance to the first autonomous vehicle.

The circuitry may be further configured to receive first sensor datafrom the first autonomous vehicle. The circuitry may be furtherconfigured to receive second sensor data from the one or more thirdautonomous vehicles. The first sensor data and the second sensor datamay include at least global positioning system (GPS) data, image data ofan exterior environment, radio detection and ranging (RADAR) data,ultrasonic data, and light detection and ranging (LiDAR) data. Thecircuitry may be further configured to retrieve at least real-time routeinformation of the at least one route from a route database, real-timeweather information of the at least one route from a weather database,and real-time traffic information of the at least one route from atraffic database. The real-time route information may include at least aroute segment type, speed restriction information, and obstructioninformation of each route segment of the at least one route. Thereal-time traffic information may include real-time traffic conditionsassociated with each route segment of the at least one route. Thereal-time weather information may include at least real-timetemperature, fog, light, humidity, and pollution information associatedwith each route segment of the at least one route.

The circuitry may be further configured to dynamically update theautonomy level of each route segment of the at least one route based onat least one of the first sensor data, the second sensor data, thereal-time route information, the real-time traffic information, and thereal-time weather information. The circuitry may be further configuredto dynamically update the autonomy level of each route segment of the atleast one route based on a vehicle category of at least one of the firstautonomous vehicle and the one or more third autonomous vehicles. Thecircuitry may be further configured to dynamically update the autonomylevel of each route segment of the at least one route based on anoperating state of one or more sensors and processing components of atleast one of the first autonomous vehicle and the one or more thirdautonomous vehicles. By dynamically updating the digital autonomy mapgenerated for the first autonomous vehicle, the transit operations ofthe first autonomous vehicle between the source location and thedestination location may be controlled and managed in an effective andefficient manner.

Thus, for deployment of the first autonomous vehicle for a trip by atransport service provider (e.g., a cab service provider such as OLA),various factors (such as conditions of the selected route for the trip,weather conditions associated with the selected route, health of thefirst autonomous vehicle, traffic conditions associated with theselected route, necessity of a driver for driving the first autonomousvehicle, and the like) may be important. The deployment of the firstautonomous vehicle along with the driver may not be always feasible dueto the increased operating cost. Thus, the generation of the digitalautonomy map and the dynamic update of the generated digital autonomymap after a regular interval of time may facilitate an efficient andeffective planning of the first autonomous vehicle for the trip prior tothe deployment of the first autonomous vehicle for the trip. Suchefficient and effective planning may assist the transport serviceprovider to optimize utilization of available resources, improve usercomfort and safety, and thereby improve cost effectiveness. Thedisclosed method and system facilitate an efficient, effective, andcomprehensive way of generating and updating the digital autonomy mapfor the first autonomous vehicle.

FIG. 1 is a block diagram that illustrates an environment 100 forgenerating a digital autonomy map, in accordance with an exemplaryembodiment of the disclosure. The environment 100 includes a map server102, a transportation server 104, a database server 106, and anavigation device 108 of an autonomous vehicle 110 associated with auser 112. The environment 100 further includes autonomous vehicles 114and autonomous vehicles 116. In an embodiment, the map server 102, thetransportation server 104, the database server 106, the navigationdevice 108, the autonomous vehicle 110, the autonomous vehicles 114, andthe autonomous vehicles 116 may be coupled to each other via acommunication network 118.

The map server 102 may include suitable logic, circuitry, interfaces,and/or code, executable by the circuitry, that may be configured toperform one or more operations for generating the digital autonomy mapand dynamically updating the digital autonomy map. The map server 102may be a computing device, which may include a software framework, thatmay be configured to create the map server implementation and performthe various operations associated with the digital autonomy map. The mapserver 102 may be realized through various web-based technologies, suchas, but not limited to, a Java web-framework, a .NET framework, aprofessional hypertext preprocessor (PHP) framework, a python framework,or any other web-application framework. The map server 102 may also berealized as a machine-learning model that implements any suitablemachine-learning techniques, statistical techniques, or probabilistictechniques. Examples of such techniques may include expert systems,fuzzy logic, support vector machines (SVM), Hidden Markov models (HMMs),greedy search algorithms, rule-based systems, Bayesian models (e.g.,Bayesian networks), neural networks, decision tree learning methods,other non-linear training techniques, data fusion, utility-basedanalytical systems, or the like. Examples of the map server 102 mayinclude, but are not limited to, a personal computer, a laptop, or anetwork of computer systems.

In an embodiment, the map server 102 may be configured to communicatewith the transportation server 104 to identify a source location and adestination location associated with the autonomous vehicle 110. The mapserver 102 may be further configured to extract historical autonomylevels associated with one or more route segments of one or more routesof a geographical region including the source location and thedestination location. The historical autonomy levels may be extractedfrom the database server 106.

In an embodiment, the map server 102 may be further configured togenerate the digital autonomy map based on at least the extractedhistorical autonomy levels. The digital autonomy map may be furthergenerated based on at least one of a vehicle category and sensorconfiguration associated with the autonomous vehicle 110. Differentcategories of autonomous vehicles may have different autonomy maps eventhough the different categories of autonomous vehicles have to followthe same route to reach the destination location. The generated digitalautonomy map (hereinafter, “the digital autonomy map”) may include theone or more route segments of at least a first route selected from theone or more routes. Each route segment on the digital autonomy map maybe tagged with an autonomy level. The digital autonomy map may beutilized by the autonomous vehicle 110 to navigate between the sourcelocation and the destination location.

In an embodiment, the map server 102 may be further configured toreceive real-time information, such as real-time autonomy levels fromthe autonomous vehicles 116, real-time first sensor data (hereinafter,“the first sensor data”) from the autonomous vehicle 110, real-timesecond sensor data (hereinafter, “the second sensor data”) from theautonomous vehicles 116, real-time route information of the first routefrom the database server 106, real-time weather information of the firstroute from the database server 106, and real-time traffic information ofthe first route from the database server 106. The map server 102 may befurther configured to dynamically update the digital autonomy level ofeach route segment of the first route based on the received real-timeinformation. The map server 102 may be further configured to dynamicallyupdate the digital autonomy level of each route segment of the firstroute based on a vehicle category of at least one of the autonomousvehicle 110 and the autonomous vehicles 116. The map server 102 may befurther configured to dynamically update the digital autonomy level ofeach route segment of the first route based on an operating state of oneor more sensors and processing components of at least one of theautonomous vehicle 110 and the autonomous vehicles 116. In anembodiment, the map server 102 may be further configured to provide oneor more navigation instructions and commands to the autonomous vehicle110 to navigate between the source location and the destinationlocation. Various operations of the map server 102 have been describedin detail in conjunction with FIGS. 2, 5A-5B, 6A-6B, and 7.

The transportation server 104 may include suitable logic, circuitry,interfaces, and/or code, executable by the circuitry, that may beconfigured to perform one or more operations associated with fleetmanagement and allocation. The transportation server 104 may be acomputing device, which may include a software framework, that may beconfigured to create the transportation server implementation andperform the various operations associated with the fleet management andallocation. The transportation server 104 may be realized throughvarious web-based technologies, such as, but not limited to, a Javaweb-framework, a .NET framework, a PHP framework, a python framework, orany other web-application framework. Examples of the transportationserver 104 may include, but are not limited to, a personal computer, alaptop, or a network of computer systems.

In an embodiment, the transportation server 104 may be configured toprocess, control, and manage various functionalities and operationsassociated with the fleet management and allocation, such as bookingrequest reception, route identification, route selection, faredetermination, vehicle selection, vehicle allocation, driver selection,and driver allocation. For example, the transportation server 104 mayselect the first route from the one or more routes based on at least anoverall autonomy level of each route, real-time traffic conditions ofeach route, real-time environmental conditions of each route, a triptime associated with each route, and route preferences of the user 112associated with the autonomous vehicle 110. Further, the transportationserver 104 may select the driver from a set of available drivers basedon at least the autonomy level of each route segment of the first route.

The transportation server 104 may be further configured to determine thesource location and the destination location associated with theautonomous vehicle 110. The source location and the destination locationmay be determined based on at least one of the user 112, currentposition information of the autonomous vehicle 110, and currentallocation status of the autonomous vehicle 110. The user 112 may be atleast one of a driver or a passenger associated with the autonomousvehicle 110. For example, when a driver is not assigned to theautonomous vehicle 110 for a trip and the autonomous vehicle 110 isallocated to the passenger by the transportation server 104, theautonomous vehicle 110 may travel from a current location (i.e., a firstsource location of the autonomous vehicle 110) to a pick-up location(i.e., a first destination location of the autonomous vehicle 110) ofthe passenger. Thereafter, when the trip starts, the autonomous vehicle110 may travel from the pick-up location (i.e., a second source locationof the autonomous vehicle 110) to a drop-off location (i.e., a seconddestination location of the autonomous vehicle 110) of the passenger. Inanother example, when the driver is assigned to the autonomous vehicle110 for the trip requested by the passenger and the driver is notpresent at a current location of the autonomous vehicle 110, theautonomous vehicle 110 may travel from the current location (i.e., afirst source location of the autonomous vehicle 110) to a first pick-uplocation (i.e., a first destination location of the autonomous vehicle110) of the driver. Further, after picking the driver, the autonomousvehicle 110 may travel from the first pick-up location (i.e., a secondsource location of the autonomous vehicle 110) to a second pick-uplocation (i.e., a second destination location of the autonomous vehicle110) of the passenger. Thereafter, when the trip starts, the autonomousvehicle 110 may travel from the second pick-up location (i.e., a thirdsource location of the autonomous vehicle 110) to a drop-off location(i.e., a third destination location of the autonomous vehicle 110) ofthe passenger.

In an embodiment, the transportation server 104 may be configured toallocate the autonomous vehicle 110 to the user 112 (such as thepassenger) based on the source location and the destination location ofeach of the autonomous vehicle 110 and the user 112. Upon allocation ofthe autonomous vehicle 110 to the user 112, the transportation server104 may be configured to generate the historical autonomy levels of theone or more routes segments of the one or more routes connecting thesource location with the destination location. The historical autonomylevels may be generated based on the historical configuration of one ormore sensors and processing components of the autonomous vehicles 114and the historical driving conditions of the one or more route segmentsof the one or more routes. The historical driving conditions may bedetermined based on historical weather, traffic, or route conditions ofthe one or more route segments of the one or more routes. Further, thehistorical autonomy levels may be generated based on a time or a timeduration of a day associated with the autonomous vehicle 110 for thetransit operations between the source location and the destinationlocation. Upon generation of the historical autonomy levels, thetransportation server 104 may store the generated historical autonomylevels in the database server 106.

In an embodiment, the transportation server 104 may be configured toprocess other services and requests associated with a trip, andaccordingly, may be configured to control, modify, and execute the otherservices and requests prior to the start of the trip or during the trip.In an embodiment, the transportation server 104 may be configured toreceive a query from the map server 102 via the communication network118. The query may be an encrypted message that is decoded by thetransportation server 104 to determine one or more requests (initiatedby the map server 102) for retrieving requisite information (such assensor information, weather information, traffic information, routeinformation, allocation information, autonomy information, or anycombination thereof). In response to the determined one or morerequests, the transportation server 104 may retrieve and communicate therequested information to the map server 102 via the communicationnetwork 118. Various operations of the transportation server 104 havebeen described in detail in conjunction with FIGS. 3, 6A-6B, and 7.

The database server 106 may include suitable logic, circuitry,interfaces, and/or code, executable by the circuitry, that may beconfigured to perform one or more database operations, such asreceiving, storing, processing, and transmitting queries, data, orcontent. The database server 106 may be a data management and storagecomputing device that is communicatively coupled to the map server 102,the transportation server 104, the navigation device 108, the autonomousvehicle 110, the autonomous vehicles 114, and the autonomous vehicles116 via the communication network 118 to perform the one or moreoperations. Examples of the database server 106 may include, but are notlimited to, a personal computer, a laptop, or a network of computersystems.

In an embodiment, the database server 106 may be configured to manageand store various types of information such as real-time and historicalroute information of the one or more routes, real-time and historicaltraffic information associated with the one or more routes, real-timeand historical weather information associated with the one or moreroutes, real-time and historical autonomy information associated withthe one or more route segments of the one or more routes, and the like.In an embodiment, the database server 106 may be further configured tomanage and store real-time position information of various autonomousvehicles such as the autonomous vehicle 110 and the autonomous vehicles116. In an embodiment, the database server 106 may be further configuredto manage and store real-time allocation status information andallocation information of the various autonomous vehicles. Theallocation status information of each autonomous vehicle (such as theautonomous vehicle 110) may indicate whether each autonomous vehicle isavailable for new allocation or not corresponding to a new bookingrequest. The allocation information of each autonomous vehicle (such asthe autonomous vehicle 110) may indicate the driver, the passenger, thesource location, the destination location, and the route informationassociated with each autonomous vehicle. In an embodiment, the databaseserver 106 may be further configured to manage and store real-time andhistorical navigation information of the various autonomous vehicles.

In an embodiment, the database server 106 may be further configured tomanage and store the first sensor data received from the autonomousvehicle 110 and the second sensor data received from the autonomousvehicles 116. The first sensor data and the second sensor data mayinclude at least global positioning system (GPS) data, image data of anexterior environment, radio detection and ranging (RADAR) data,ultrasonic data, and light detection and ranging (LiDAR) data associatedwith the autonomous vehicle 110 and the autonomous vehicles 116,respectively. In an embodiment, the database server 106 may be furtherconfigured to receive a query from the map server 102 or thetransportation server 104 via the communication network 118. The querymay be an encrypted message that is decoded by the database server 106to determine one or more requests for retrieving requisite information(such as sensor information, weather information, traffic information,route information, allocation information, autonomy information, or anycombination thereof). In response to the determined one or morerequests, the database server 106 may be configured to retrieve andcommunicate the requested information to the map server 102 or thetransportation server 104 via the communication network 118. Variousoperations of the database server 106 have been described in detail inconjunction with FIGS. 4 and 7.

The navigation device 108 may include suitable logic, circuitry,interfaces and/or code, executable by the circuitry, that may beconfigured to perform one or more navigation operations. For example,the navigation device 108 may be a computing device that is utilized, bythe autonomous vehicle 110 or the user 112 (such as the driver of theautonomous vehicle 110), to initiate a navigation request for navigatingbetween the source location and the destination location. In anembodiment, the navigation device 108 may be configured to receive, fromthe map server 102 or the transportation server 104 via thecommunication network 118, one or more navigation interfaces that allowthe autonomous vehicle 110 or the user 112 to interact with one or morecomputing devices, servers, or applications for performing the one ormore navigation operations. Each navigation interface may include thedigital autonomy map generated for the corresponding autonomous vehiclesuch as the autonomous vehicle 110. The digital autonomy map may presenta satellite image of the geographical region including various routesegments such as the one or more routes segments of the one or moreroutes associated with the source location and the destination location.Further, the digital autonomy map may present the autonomy level of eachroute segment that is dynamically updated in real-time by the map server102 during the transit operation of the autonomous vehicle 110.

In an embodiment, the navigation device 108 may receive the one or morenavigation interfaces based on the navigation request initiated by theuser 112. In another embodiment, the navigation device 108 mayautomatically receive the one or more navigation interfaces based on theallocation of the autonomous vehicle 110 to the user 112 (such as thepassenger) for the trip between the source location and the destinationlocation of the passenger. Upon reception of the one or more navigationinterfaces, the navigation device 108 may be utilized, by the autonomousvehicle 110 or the user 112 (such as the driver), to navigate betweenthe source location and the destination location. In an embodiment, theautonomous vehicle 110 may utilize the digital autonomy map (along withthe related navigation instructions and commands) associated with theone or more navigation interfaces to automatically transit between thesource location and the destination location with or without anyassistance from the user 112 (such as the driver). During the transitoperation, the autonomous vehicle 110 may switch between the autonomylevels, and thus may control (i.e., increase or decrease) variousvehicle dynamics such as propulsion, breaking, steering, speed,acceleration, or deacceleration associated with the autonomous vehicle110 based on at least the autonomy level associated with each routesegment of the first route. In another embodiment, when the autonomousvehicle 110 is being driven by the user 112 from the source location, anavigation interface may be utilized, by the user 112, to interact andprovide one or more inputs for initiating the one or more operationsassociated with the navigation request. For example, the navigationdevice 108 may be utilized, by the user 112, to input the sourcelocation and the destination location for viewing the one or more routesconnecting the source location with the destination location. Further,the navigation device 108 may be utilized, by the user 112, to selectthe first route from the one or more routes. Further, the navigationdevice 108 may be utilized, by the user 112, to view the autonomy levelassociated with each of the one or more route segments of the firstroute. Based on the autonomy level associated with each of the one ormore route segments of the first route, the autonomous vehicle 110 maybe partially or fully controlled, by the user 112, during the transitoperation along the one or more route segments of the first route. Incase of the fully autonomous mode, the transit operation is notintervened by the user 112, and the autonomous vehicle 110 may performself-driving on its own during the transit operation along the one ormore route segments of the first route. Various operations of thenavigation device 108 have been described in detail in conjunction withFIGS. 2, 5A-5B, 6A-6B, and 7.

The autonomous vehicle 110 is a mode of transportation that is utilized,by the user 112 (such as the driver or the passenger), to commute fromone location to another location. The autonomous vehicle 110 may includesuitable logic, circuitry, interfaces and/or code, executable by thecircuitry, that may be configured to control and perform one or moreself-driving operations with or without any driving assistance from thedriver associated the autonomous vehicle 110. In one embodiment, theautonomous vehicle 110 may be a self-driving vehicle deployed by atransport service provider to cater to travelling requirements ofvarious passengers. In another embodiment, the autonomous vehicle 110may be a self-driving vehicle that is privately owned by an individualsuch as the user 112. Examples of the autonomous vehicle 110 may includea car, a bus, an auto-rickshaw, or the like.

In an embodiment, the autonomous vehicle 110 may include variouscomponents (such as sensors, processors, and/or controllers), andsoftware framework executed by the components for implementing variousaspects of vehicle motion (such as propulsion, breaking, acceleration,steering, or the like) and auxiliary behavior (such as controllinglights, controlling temperature, or the like) of the autonomous vehicle110. Examples of the sensors may include one or more LiDAR sensors, oneor more RADAR sensors, one or more image acquiring modules, one or moreinfrared (IR) sensors, one or more location sensors, one or moreultrasonic sensors, and/or the like. Examples of the controllers mayinclude one or more speed controllers, one or more temperaturecontrollers, one or more throttle controllers, and/or the like. Theperformance of the autonomous vehicle 110 may be based on aconfiguration of the components of the autonomous vehicle 110. In anembodiment, the autonomous vehicle 110 may be configured to transmit thefirst sensor data to the map server 102, the transportation server 104,or the database server 106 via the communication network 118. The firstsensor data may include at least the GPS data, the image data, the RADARdata, the ultrasonic data, and the LiDAR data, along with configurationinformation associated with the one or more sensors and processingcomponents of the autonomous vehicle 110. The autonomous vehicle 110 maybe further configured to transmit the vehicle dynamics data, such aspropulsion data, breaking data, acceleration data, steering data, or thelike, associated with the one or more route segments of the one or moreroutes to the map server 102, the transportation server 104, or thedatabase server 106 via the communication network 118. The autonomousvehicle 110 may be further configured to transmit operating state dataof the one or more sensors and processing components to the map server102, the transportation server 104, or the database server 106 via thecommunication network 118. The operating state data may indicate thecurrent operational status of the one or more sensors and processingcomponents of the autonomous vehicle 110. For example, an operationalstatus of the LiDAR sensor may represent whether the LiDAR sensor iscurrently ON or OFF. The operational status of the LiDAR sensor may alsorepresent whether the LiDAR sensor is currently malfunctioning orfunctioning without any flaw. Further, in an embodiment, the firstsensor data, the configuration information, the vehicle dynamics data,and/or the operating state data associated with the autonomous vehicle110 may be utilized, by the map server 102, to dynamically update thedigital autonomy map in real-time.

The autonomous vehicles 114 and 116 are one or more autonomous vehicleshaving similar functionalities and operations as described above withrespect to the autonomous vehicle 110. For simplicity of the disclosure,it has been assumed that the autonomous vehicles 114 are one or morehistorical autonomous vehicles that may have previously traversed alongthe one or more route segments of the one or more routes connecting thesource location with the destination location of the autonomous vehicle110. Thus, the historical autonomy levels may be generated based on atleast the historical configuration of the one or more sensors andprocessing components of the autonomous vehicles 114 and the historicaldriving conditions of the one or more route segments of the one or moreroutes. It has been further assumed that the autonomous vehicles 116 areone or more autonomous vehicles that may be currently traversing the oneor more route segments of the one or more routes and may be ahead of theautonomous vehicle 110. In an embodiment, each of the autonomousvehicles 116 may be configured to transmit the second sensor data to themap server 102, the transportation server 104, or the database server106 via the communication network 118. The second sensor data mayinclude at least the GPS data, the image data, the RADAR data, theultrasonic data, and the LiDAR data, along with configurationinformation associated with the one or more sensors and processingcomponents of each of the autonomous vehicles 116. Each of theautonomous vehicles 116 may be further configured to transmit vehicledynamics data, such as propulsion data, breaking data, accelerationdata, steering data, or the like, associated with the one or more routesegments of the one or more routes to the map server 102, thetransportation server 104, or the database server 106 via thecommunication network 118. Each of the autonomous vehicles 116 may befurther configured to transmit operating state data of the one or moresensors and processing components to the map server 102, thetransportation server 104, or the database server 106 via thecommunication network 118. The operating state data may indicate thecurrent operational status of the one or more sensors and processingcomponents of each of the autonomous vehicles 116. For example, anoperational status of the RADAR sensor may represent whether the RADARsensor is currently ON or OFF. The operational status of the RADARsensor may also represent whether the RADAR sensor is currentlymalfunctioning or functioning without any flaw. Further, in anembodiment, the second sensor data, the configuration information, thevehicle dynamics data, and/or the operating state data associated withthe autonomous vehicles 116 may be utilized, by the map server 102, todynamically update the digital autonomy map in real-time.

The communication network 118 may include suitable logic, circuitry,interfaces, and/or code, executable by the circuitry, that may beconfigured to transmit queries, messages, data, and requests betweenvarious entities, such as the map server 102, the transportation server104, the database server 106, the autonomous vehicle 110, the autonomousvehicles 114, and/or the autonomous vehicles 116. Examples of thecommunication network 118 may include, but are not limited to, awireless fidelity (Wi-Fi) network, a light fidelity (Li-Fi) network, alocal area network (LAN), a wide area network (WAN), a metropolitan areanetwork (MAN), a satellite network, the Internet, a fiber optic network,a coaxial cable network, an infrared (IR) network, a radio frequency(RF) network, and a combination thereof. Various entities in theenvironment 100 may be coupled to the communication network 118 inaccordance with various wired and wireless communication protocols, suchas Transmission Control Protocol and Internet Protocol (TCP/IP), UserDatagram Protocol (UDP), Long Term Evolution (LTE) communicationprotocols, or any combination thereof.

In operation, the transportation server 104 may be configured to receivethe navigation request from the autonomous vehicle 110. The navigationrequest may include the source location and the destination locationassociated with the autonomous vehicle 110 or the user 112 such as thedriver or the passenger. In another embodiment, the transportationserver 104 may be configured to generate the navigation request for theautonomous vehicle 110 based on the allocation of the autonomous vehicle110 to the user 112.

In an embodiment, the transportation server 104 may be configured togenerate the historical autonomy levels for the one or more routesegments of the one or more routes. Each route of the one or more routesmay connect the source location with the destination location. Thehistorical autonomy levels may be generated based on at least thehistorical configuration of the one or more sensors and processingcomponents of the autonomous vehicles 114 and the historical drivingconditions associated with the one or more route segments. Thehistorical autonomy levels may be further generated based on historicalvehicle dynamics data (such as historical propulsion data, historicalbreaking data, historical acceleration data, historical steering data,or the like) of the autonomous vehicles 114. Further, the historicalautonomy levels for each route segment may be generated based on atleast one of the vehicle category of each autonomous vehicle and theoperating state of the one or more sensors and processing components ofeach of the autonomous vehicles 114. For example, 5 autonomous vehiclesof the same vehicle category (for example, sedan vehicles) traversedalong a first route segment between 10:50 AM to 11:00 AM. In 2autonomous vehicles, only 4 sensors out of 5 sensors were active. Inanother 2 autonomous vehicles, only 3 sensors out of 5 sensors wereactive. In 1 autonomous vehicle, all of the 5 sensors were active. Insuch a scenario, the historical autonomy levels may be generated basedon one or more predefined rules. For example, for an autonomous vehiclewith 90-100 percent active sensors, the historical autonomy level may bedetermined as 5. Further, for an autonomous vehicle with 80-90 percentactive sensors, the historical autonomy level may be determined as 4.Further, for an autonomous vehicle with 70-80 percent active sensors,the historical autonomy level may be determined as 3. Further, for anautonomous vehicle with 60-70 percent active sensors, the historicalautonomy level may be determined as 3, and so on. Thus, in the currentexemplary scenario, the historical autonomy level for each of the 2autonomous vehicles with 4 active sensors may be determined as 4.Similarly, the historical autonomy level for each of the 2 autonomousvehicles with 3 active sensors may be determined as 3, and thehistorical autonomy level for the 1 autonomous vehicle with 5 activesensors may be determined as 5. Upon generation of the historicalautonomy levels, the transportation server 104 may store the historicalautonomy levels associated with the one or more route segments of theone or more routes in the database server 106.

In an embodiment, in response to the navigation request, the map server102 may be configured to extract, from the database server 106, thehistorical autonomy levels associated with the one or more routesegments of the one or more routes. The historical autonomy levels maybe extracted from the database server 106 based on the time or the timeduration of the day associated with the autonomous vehicle 110 for thecurrent transit operations between the source location and thedestination location. The historical autonomy levels may also beextracted from the database server 106 based on the vehicle category ofthe autonomous vehicle 110. In an embodiment, the map server 102 may befurther configured to determine the autonomy level of each of the one ormore route segments of the one or more routes based on the historicalautonomy levels associated with the one or more route segments of theone or more routes. For example, the autonomy level for a route segmentfor a time duration may be generated based on a statistical value (e.g.,an average value) of the historical autonomy levels of the autonomousvehicles 114 associated with the same time duration and the same routesegment. For example, 5 autonomous vehicles traversed along a secondroute segment between 10:10 AM to 10:20 AM and the historical autonomylevels of the 5 autonomous vehicles were generated as 5, 4, 5, 3, and 3.In such a scenario, the autonomy level for the first route segmentbetween the 10:10 AM to 10:20 AM may be determined as an average valueof the autonomy levels 5, 4, 5, 3, and 3 i.e., 4.

In an embodiment, the map server 102 may be further configured togenerate the digital autonomy map for the autonomous vehicle 110. Thedigital autonomy map may include the one or more route segments of atleast the first route connecting the source location with thedestination location. The first route may be selected, by thetransportation server 104, from the one or more routes based on at leastone of the overall autonomy level of each route, the real-time trafficconditions along each route, the real-time environmental conditionsalong each route, the trip time associated with each route, or the routepreferences of the user 112 associated with the autonomous vehicle 110.The user 112 may be at least one of the driver or the passengerassociated with the autonomous vehicle 110. The driver may be selectedfrom the set of available drivers based on at least the autonomy levelof each route segment of the at least first route. For example, if it isdetermined that the autonomy level of the all the route segments of thefirst route is either 4 or 5, and thus the autonomous vehicle 110 doesnot require any driving assistance from the driver, then the driver isnot selected for the autonomous vehicle 110. However, if it isdetermined that the autonomy level of at least one route segment of thefirst route is 3 or less, then the driver is selected for from the setof available drivers for providing the driving assistance to theautonomous vehicle 110.

In an embodiment, the map server 102 may be further configured to tageach route segment of the first route based on the determined autonomylevel. Thus, the map server 102 may generate the digital autonomy mapsuch that each route segment on the digital autonomy map is tagged withthe determined autonomy level. For example, there are 3 route segments(such as a first route segment, a second route segment, and a thirdroute segment) of the first route that is connecting the source locationwith the destination location. Further, the autonomy levels of the firstroute segment, the second route segment, and the third route segment aredetermined as 4, 5, and 4. The map server 102 may generate the digitalautonomy map including at least visual representation of the first routesegment, the second route segment, and the third route segment of thefirst route such that each of the first route segment, the second routesegment, and the third route segment is tagged with the autonomy levels4, 5, and 4, respectively. The tagging may be executed by usingnumber-based tagging, color-based tagging, voice-based tagging, or anycombination thereof.

In an exemplary embodiment, the number-based tagging of a route segment(such as the first route segment) may be executed by tagging the routesegment using an integer such as “0”, “1”, “2”, “3”, “4”, or “5”. Theinteger “0” may correspond to an “autonomy level 0” that does notinclude automated assistance. The integer “1” may correspond to an“autonomy level 1” that includes the driver assistance and at least oneadvanced driver-assistance feature such as adaptive cruise control. Theinteger “2” may correspond to an “autonomy level 2” that includespartial automation having at least two advanced driver-assistancesystems (ADAS) that can at times control the braking, steering, oracceleration of the autonomous vehicle (such as the autonomous vehicle110). The integer “3” may correspond to an “autonomy level 3” thatincludes conditional automation having full control during select partsof a journey based on one or more driving conditions. The integer “4”may correspond to an “autonomy level 4” that includes high automationvehicle in which the autonomous vehicle 110 may be capable of completingan entire journey without driver intervention, but the autonomousvehicle 110 does have some operating constraints. For example, a Level 4autonomous vehicle may be confined to a certain geographical area (i.e.,geofenced), or the Level 4 autonomous vehicle may be prohibited fromoperating beyond a certain speed. The Level 4 autonomous vehicle maylikely still maintain driver controls like a steering wheel and pedalsfor those instances in which a human may be required to assume control.The integer “5” may correspond to an “autonomy level 5” that includesfull automation and does not require driver intervention at all. TheLevel 5 autonomous vehicle may be capable of complete hands-off,driverless operation under all circumstances.

In an exemplary embodiment, the color-based tagging of a route segment(such as the first route segment) may be executed by tagging (forexample, coloring on the digital autonomy map) the route segment using acolor such as “red”, “purple”, “blue”, “yellow”, “orange”, or “green”.The color “red” may correspond to an “autonomy level 0”. The color“purple” may correspond to an “autonomy level 1”. The color “blue” maycorrespond to an “autonomy level 2”. The color “yellow” may correspondto an “autonomy level 3”. The color “orange” may correspond to an“autonomy level 4”. The color “green” may correspond to an “autonomylevel 5”.

In an exemplary embodiment, the voice-based tagging of a route segment(such as the first route segment) may be executed by tagging the routesegment using a voice command that indicates one of an “autonomy level0”, an “autonomy level 1”, an “autonomy level 2”, an “autonomy level 3”,an “autonomy level 4”, or an “autonomy level 5”.

Upon generation of the digital autonomy map for the autonomous vehicle110, the map server 102 may be configured to render a navigationinterface on the display of the navigation device 108 of the autonomousvehicle 110 via the communication network 118. The navigation interfacemay present the digital autonomy map generated for the autonomousvehicle 110. The digital autonomy map may be utilized by the autonomousvehicle 110 for controlling and managing various transit and vehicledynamics operations between the source location and the destinationlocation. For example, the autonomous vehicle 110 may switch from afirst autonomy level associated with a first route segment to a secondautonomy level associated with a second route segment, when theautonomous vehicle 110 is making the transition from the first routesegment to the second route segment during the trip. In accordance withsuch switching, the autonomous vehicle 110 may be configured to controland manage various vehicle dynamics such as propulsion, breaking,steering, speed, acceleration, or deacceleration associated with theautonomous vehicle 110.

In an embodiment, the map server 102 may be configured to receive thereal-time autonomy levels from the autonomous vehicles 116 that may becurrently traversing via the one or more route segments of the firstroute. Based on the received real-time autonomy levels, the map server102 may be configured to dynamically update the autonomy level of eachroute segment of the first route on the digital autonomy map, therebyproviding real-time driving assistance to the autonomous vehicle 110. Inan embodiment, the autonomy level may be dynamically updated for onlythose route segments or parts of the route segments of the first routethat have not been traversed by the autonomous vehicle 110.

In an embodiment, the map server 102 may be further configured toreceive the first sensor data from the autonomous vehicle 110 and thesecond sensor data from the autonomous vehicles 116. The first sensordata and the second sensor data may include at least the GPS data, theimage data, the RADAR data, the ultrasonic data, and the LiDAR data. Themap server 102 may be further configured to receive the real-time routeinformation of the first route, the real-time weather information of thefirst route, and the real-time traffic information of the first routefrom the database server 106. The real-time route information mayinclude at least a route segment type, speed restriction information,and obstruction information of each route segment of the first route.The real-time traffic information may include real-time trafficconditions associated with each route segment of the first route. Thereal-time weather information may include at least real-timetemperature, fog, light, humidity, and pollution information associatedwith each route segment of the first route. In an embodiment, the mapserver 102 may be further configured to dynamically update the autonomylevel of each route segment (that has not been traversed by theautonomous vehicle 110) of the first route based on at least one of thefirst sensor data, the second sensor data, the real-time routeinformation, the real-time traffic information, and the real-timeweather information. For example, a current autonomy level of theautonomous vehicle 110 is 5 along a first route segment, and autonomylevels for subsequent route segments are set as 4, 5, and 4. The mapserver 102 receives the first sensor data from the autonomous vehicle110 and determines that a LiDAR sensor of the autonomous vehicle 110 iscurrently not operating. In such a scenario, the map server 102 maydowngrade the autonomy level of each route segment of the first route.For example, the current autonomy level along the first route segmentmay be downgraded from 5 to 4, and the autonomy levels for thesubsequent route segments may be downgraded from 4, 5, and 4 to 3, 4,and 3, respectively. In another example, the current autonomy levels setfor a first route segment, a second route segment, and a third routesegment are 4, 5, and 3, and the autonomous vehicle 110 is currentlytraversing along the first route segment. The map server 102 receivesthe second sensor data from the autonomous vehicles 116 and determinesan occurrence of an accident along the second route segment based on atleast the second sensor data such as the image data of the exteriorenvironment of the second route segment. In such a scenario, the mapserver 102 may downgrade the autonomy level of the second route segmentfrom 5 to 3. In another example, the current autonomy levels set for afirst route segment, a second route segment, and a third route segmentare 3, 3, and 3, and the autonomous vehicle 110 is currently traversingalong the first route segment. The map server 102 receives the real-timeweather information and determines that the second route segment and thethird route segment have pleasant weather conditions with a lightintensity greater than a minimum threshold value with no fog. Also,route visibility is more than what is required for driving along thesecond route segment and the third route segment. In such a scenario,the map server 102 may upgrade the autonomy level of the second routesegment and the third route segment from 3 and 3 to 5 and 4,respectively.

In an embodiment, the map server 102 may be further configured todynamically update the autonomy level of each route segment (that hasnot been traversed by the autonomous vehicle 110) of the first routebased on the vehicle category of at least one of the autonomous vehicle110 and the autonomous vehicles 116. For example, the current autonomylevels set for a first route segment, a second route segment, and athird route segment are 3, 4, and 3, and the autonomous vehicle 110 iscurrently traversing along the first route segment. The autonomousvehicle 110 is associated with a first vehicle category, for example, amini hatchback vehicle category. The map server 102 may determine thatthe autonomous vehicles 116 are currently traversing along the secondroute segment of the same first route and are ahead of the autonomousvehicle 110. Further, the autonomous vehicles 116 are associated with asecond vehicle category, for example, a prime sedan vehicle category.Since the vehicle category of the autonomous vehicle 110 and theautonomous vehicles 116 is different from each other, any change orupdate with respect to the autonomy level may not be in a proportionateorder. For example, if the autonomy level of the autonomous vehicles 116change from 5 to 4 while traversing along the second route segment, thenthe autonomy level for the autonomous vehicle 110 along the second routesegment may be updated from 4 to 2. Further, in an embodiment, the mapserver 102 may be configured to dynamically update the autonomy level ofeach route segment (that has not been traversed by the autonomousvehicle 110) of the first route based on the operating state (i.e., thecurrent working status) of the one or more sensors and processingcomponents of at least one of the autonomous vehicle 110 and theautonomous vehicles 116. For example, the current autonomy levels setfor a first route segment, a second route segment, and a third routesegment are 3, 4, and 3. Further, the autonomous vehicle 110 has 3sensors such as a LiDAR sensor, a RADAR sensor, and an image senor. Themap server 102 determines that the image sensor is currently not workingdue to some malfunction. In such a scenario, the map server 102 maydowngrade the autonomy levels set for the first route segment, thesecond route segment, and the third route segment from 3, 4, and 3 to 2,2, and 2, respectively. Based on the various update of the autonomylevels during the trip, the digital autonomy map may be utilized by theautonomous vehicle 110 and/or the user 112 (such as the driver) forcontrolling and managing the various transit and vehicle dynamicsoperations between the source location and the destination location.

In an embodiment, the map server 102 may be further configured tocommunicate the one or more navigation instructions and commands to theautonomous vehicle 110 and/or the user 112 (such as the driver) tonavigate the autonomous vehicle 110 along the one or more route segmentsof the first route. The one or more navigation instructions and commandsmay be communicated in the form of text messages, audio signals, videosignals, or any combination thereof, and may include point-by-pointdirections for navigating along the first route. In another embodiment,the transportation server 104 may be configured to provide a drivingplan (including one or more driving instructions) to the user 112 of theautonomous vehicle 110 based on the received the real-time routeinformation, the real-time traffic information, and the real-timeweather information. The transportation server 104 may be furtherconfigured to provide notifications on the navigation device 108 foralerting the autonomous vehicle 110 or the user 112 (such as the driver)of the autonomous vehicle 110 while switching between the routesegments.

FIG. 2 is a block diagram that illustrates the map server 102, inaccordance with an exemplary embodiment of the disclosure. The mapserver 102 includes circuitry such as a processor 202, a memory 204, anextractor 206, a map generator 208, a navigation engine 210, and atransceiver 212 that communicate with each other via a firstcommunication bus (not shown).

The processor 202 may include suitable logic, circuitry, interfaces,and/or code, executable by the circuitry, that may be configured toperform one or more operations for generating and rendering the digitalautonomy map. Examples of the processor 202 may include, but are notlimited to, an application-specific integrated circuit (ASIC) processor,a reduced instruction set computing (RISC) processor, a complexinstruction set computing (CISC) processor, and a field-programmablegate array (FPGA). It will be apparent to a person of ordinary skill inthe art that the processor 202 may be compatible with multiple operatingsystems.

In an embodiment, the processor 202 may be configured to retrieve thesource location and the destination location of the user 112 from thetransportation server 104 or the database server 106 for determining theone or more routes. The processor 202 may be further configured tocontrol and manage various functionalities and operations such as dataextraction and map generation. The processor 202 may be furtherconfigured to control and manage generation and presentation ofreal-time navigation instructions and commands for navigating across theone or more route segments of the one or more routes.

In an embodiment, the processor 202 may be further configured to extractthe historical autonomy levels from the transportation server 104 or thedatabase server 106. The processor may be further configured todetermine the autonomy level of each of the one or more route segmentsof the one or more routes based on at least the historical autonomylevels associated with the one or more route segments.

In an embodiment, the processor 202 may operate as a master processingunit, and the memory 204, the extractor 206, the map generator 208, andthe navigation engine 210 may operate as slave processing units. In sucha scenario, the processor 202 may be configured to instruct the memory204, the extractor 206, the map generator 208, and the navigation engine210 to perform the corresponding operations either independently or inconjunction with each other.

The memory 204 may include suitable logic, circuitry, interfaces, and/orcode, executable by the circuitry, that may be configured to store oneor more instructions that are executed by the processor 202, theextractor 206, the map generator 208, and the navigation engine 210 toperform their operations. In an exemplary embodiment, the memory 204 maybe configured to temporarily store the historical autonomy levelsassociated with the one or more route segments of the one or moreroutes. The memory 204 may be further configured to temporarily storereal-time information pertaining to traffic information, weatherinformation, and route information. The memory 204 may be furtherconfigured to temporarily store the first sensor data received from theautonomous vehicle 110, and the second sensor data received from theautonomous vehicles 116. The memory 204 may be further configured totemporarily store the vehicle category of the autonomous vehicle 110,the autonomous vehicles 114, and the autonomous vehicles 116. The memory204 may be further configured to temporarily store the operating state(i.e., the current operating status) of the one or more sensors andprocessing components associated with the autonomous vehicle 110 or eachof the autonomous vehicles 116. The memory 204 may be further configuredto temporarily store one or more sets of predefined rules and data thatcan be utilized for updating the autonomy level in real-time. The memory204 may be further configured to temporarily store the determinedautonomy levels of the one or more route segments. Examples of thememory 204 may include, but are not limited to, a random-access memory(RAM), a read-only memory (ROM), a programmable ROM (PROM), and anerasable PROM (EPROM).

The extractor 206 may include suitable logic, circuitry, interfaces,and/or code, executable by the circuitry, that may be configured toperform the one or more operations for data extraction. The extractor206 may be implemented by one or more processors, such as, but are notlimited to, an ASIC processor, a RISC processor, a CISC processor, andan FPGA processor. Further, the extractor 206 may include amachine-learning model that implements any suitable machine-learningtechniques, statistical techniques, or probabilistic techniques forperforming one or more data extraction operations.

In an exemplary embodiment, the extractor 206 may be configured toextract the historical autonomy levels from the database server 106, andstore the extracted historical autonomy levels in the memory 204. Theextractor 206 may be further configured to extract the real-timeinformation, such as the traffic information, the route information, theweather information, the first sensor data, and the second sensor data,from the database server 106, and store the real-time information in thememory 204.

The map generator 208 may include suitable logic, circuitry, interfaces,and/or code, executable by the circuitry, that may be configured toperform the one or more operations for generating the digital autonomymap. The map generator 208 may be implemented by one or more processors,such as, but are not limited to, an ASIC processor, a RISC processor, aCISC processor, and an FPGA processor. Further, the map generator 208may include a machine-learning model that implements any suitablemachine-learning techniques, statistical techniques, or probabilistictechniques for performing the one or more operations for generating thedigital autonomy map.

In an exemplary embodiment, the map generator 208 may be configured toretrieve the historical autonomy levels from the memory 204. The mapgenerator 208 may be further configured to generate the digital autonomymap such that each route segment on the digital autonomy map is taggedwith the determined autonomy level. In an exemplary embodiment, if thesource location and the destination location are the same for aplurality of autonomous vehicles, then the digital autonomy map may bethe same for the plurality of autonomous vehicles when the vehiclecategory associated with the plurality of autonomous vehicles is alsothe same. In another exemplary embodiment, if the source location andthe destination location are the same for a plurality of autonomousvehicles and the vehicle category of each of the plurality of autonomousvehicles is different, then the digital autonomy map may be differentfor each of the plurality of autonomous vehicles. In another exemplaryembodiment, if the source location and the destination location are thesame for a plurality of autonomous vehicles and the vehicle category ofeach of the plurality of autonomous vehicles is also the same, then thedigital autonomy map may be the same for each of the plurality ofautonomous vehicles when the number of sensors and processing componentsin each of the plurality of autonomous vehicles is also the same and thesensors and processing components are in operating conditions. Inanother exemplary embodiment, if the source location and the destinationlocation are the same for a plurality of autonomous vehicles and thevehicle category of each of the plurality of autonomous vehicles is alsothe same, but the number of sensors and processing components in each ofthe plurality of autonomous vehicles is different, then the digitalautonomy map may be different for each of the plurality of autonomousvehicles.

Upon generation of the digital autonomy map for each autonomous vehicle(such as the autonomous vehicle 110), the map generator 208 may beconfigured to dynamically update the digital autonomy map. For example,the digital autonomy map may be dynamically updated in real-time basedon at least one of the real-time traffic information, the real-timeroute information, the real-time weather information, the first sensordata, the second sensor data, the vehicle category, or the operatingstates of the one or more sensors and processing components.

The navigation engine 210 may include suitable logic, circuitry,interfaces, and/or code, executable by the circuitry, that may beconfigured to perform the one or more operations for navigation of theautonomous vehicle 110 along the first route. The navigation engine 210may be implemented by one or more processors, such as, but are notlimited to, an ASIC processor, a RISC processor, a CISC processor, andan FPGA processor. Further, the navigation engine 210 may include amachine-learning model that implements any suitable machine-learningtechniques, statistical techniques, or probabilistic techniques forperforming the one or more operations.

In an exemplary embodiment, the navigation engine 210 may be configuredto render the one or more navigation interfaces on the display of thenavigation device 108 based on the navigation request. A navigationinterface may be a graphical user interface (GUI) that allows an entity(such as the autonomous vehicle 110 or the user 112) to interact withthe map server 102 by utilizing information and options (e.g., graphicalicons and visual indicators) included in the GUI. In an exemplaryembodiment, the navigation engine 210 may be further configured tocommunicate the one or more navigation instructions and commands to theautonomous vehicle 110 and/or the user 112 (such as the driver) forfacilitating navigation across the one or more route segments of thefirst route. The navigation instructions may include road-by-roaddirections, and/or turn-by-turn directions for navigation of theautonomous vehicle 110 along the first route.

The transceiver 212 may include suitable logic, circuitry, interfaces,and/or code, executable by the circuitry, that may be configured totransmit (or receive) data to (or from) various servers or devices, suchas the transportation server 104, the database server 106, or thenavigation device 108. Examples of the transceiver 212 may include, butare not limited to, an antenna, a radio frequency transceiver, awireless transceiver, and a Bluetooth transceiver. The transceiver 212may be configured to communicate with the transportation server 104, thedatabase server 106, or the navigation device 108 using various wiredand wireless communication protocols, such as TCP/IP, UDP, LTEcommunication protocols, or any combination thereof.

FIG. 3 is a block diagram that illustrates the transportation server104, in accordance with an exemplary embodiment of the disclosure. Thetransportation server 104 includes a processor 302, a memory 304, avehicle detection engine 306, a data mining engine 308, a ride planningengine 310, a notification engine 312, and a transceiver 314 thatcommunicate with each other via a second communication bus (not shown).

The processor 302 may include suitable logic, circuitry, interfaces,and/or code, executable by the circuitry, that may be configured toperform one or more operations. Examples of the processor 302 mayinclude, but are not limited to an ASIC processor, a RISC processor, aCISC processor, and an FPGA. It will be apparent to a person of ordinaryskill in the art that the processor 302 is compatible with multipleoperating systems.

In an exemplary embodiment, the vehicle detection engine 306 mayconfigure the processor 302 to control and manage detection of anautonomous vehicle (such as the autonomous vehicle 110) for allocation.Further, the data mining engine 308 may configure the processor 302 tocontrol and manage extraction of requisite information from the databaseserver 106. In an exemplary embodiment, the processor 302 may beconfigured to communicate the driving plan to the user 112 of theautonomous vehicle 110 based on the extracted route information by usingthe ride planning engine 310. The processor 302 may be furtherconfigured to control and manage rendering of the one or more navigationinterfaces on the navigation device 108 of the autonomous vehicle 110 byutilizing the notification engine 312. The processor may alsocommunicate one or more notifications, instructions, or commands to thenavigation device 108 of the autonomous vehicle 110. In an embodiment,the processor 302 may be configured to generate the historical autonomylevels based on the historical configuration of the one or more sensorsand processing components of the autonomous vehicles 114 and thehistorical weather information associated with the one or more routesegments.

In an embodiment, the processor 302 may operate as a master processingunit, the memory 304, the vehicle detection engine 306, the data miningengine 308, the ride planning engine 310, and the notification engine312, may operate as slave processing units. In such a scenario, theprocessor 302 may provide instructions to the memory 304, the vehicledetection engine 306, the data mining engine 308, the ride planningengine 310, and the notification engine 312 to perform the one or moreoperations either independently or in conjunction with each other.

The memory 304 may include suitable logic, circuitry, interfaces, and/orcode, executable by the circuitry, that may be configured to store oneor more instructions that are executed by the processor 302, the vehicledetection engine 306, the data mining engine 308, the ride planningengine 310, and the notification engine 312 to perform their operations.In an exemplary embodiment, the memory 304 may be configured totemporarily store and manage the information pertaining to the variousfactors associated with one or more route segments, such as thehistorical and real-time traffic information, the historical andreal-time weather information, or the historical and real-time autonomyinformation. In an exemplary embodiment, the memory 304 may be furtherconfigured to temporarily store the booking request and navigationrequest initiated by the user 112. In an exemplary embodiment, thememory 304 may be further configured to manage and store real-timeallocation status information and allocation information of the variousautonomous vehicles. Examples of the memory 304 may include, but are notlimited to, a RAM, a ROM, a PROM, and an EPROM.

The vehicle detection engine 306 may include suitable logic, circuitry,interfaces, and/or code, executable by the circuitry, that may beconfigured to perform the one or more operations for detection of anautonomous vehicle, such as autonomous vehicle 110, for allocation tothe user 112. For example, the autonomous vehicle 110 may be detectedfrom one or more autonomous vehicles based on the real-time allocationstatus information and the real-time position information of theautonomous vehicle 110 obtained from the vehicle device (not shown) ofthe autonomous vehicle 110. The allocation status information of eachautonomous vehicle (such as the autonomous vehicle 110) may indicatewhether each autonomous vehicle is available for new allocation or notcorresponding to a new booking request. The real-time positioninformation may include GPS information that indicate the currentlocation of the autonomous vehicle 110. The vehicle detection engine 306may be implemented by one or more processors, such as, but are notlimited to, an ASIC processor, a RISC processor, a CISC processor, andan FPGA.

The data mining engine 308 may include suitable logic, circuitry,interfaces, and/or code, executable by the circuitry, that may beconfigured to perform the one or more operations for data management.For example, the data mining engine 308 may be configured to extracthistorical information pertaining to the one or more routes, such ashistorical route information, historical traffic information, historicalautonomy information, and historical weather information, from thedatabase server 106, and store the extracted historical information inthe memory 304. The data mining engine 308 may be further configured toextract the real-time allocation status information and the real-timeposition information from a vehicle device (not shown) of the autonomousvehicle 110, and store the real-time allocation status information andthe real-time position information in the memory 304. The data miningengine 308 may be implemented by one or more processors, such as, butnot limited to, an ASIC processor, a RISC processor, a CISC processor,and an FPGA.

The ride planning engine 310 may include suitable logic, circuitry,interfaces, and/or code, executable by the circuitry, that may beconfigured to perform the one or more operations for communicating thedriving plan including the one or more driving instructions to theautonomous vehicle 110. For example, the ride planning engine 310 mayprovide the one or more driving instructions for maintaining user andvehicle safety and mitigating worst-case travel scenarios, such asvehicle collision, vehicle skidding, or the like. The one or moredriving instructions are based on factors such as conditions of theroute to be traversed by the autonomous vehicle 110, the type of theroad (i.e., backroad, carriageway, highway, or the like) included in theroute, the current location of the autonomous vehicle 110, the time oftravel, the weather condition at the time of travel, or the like. In anembodiment, the ride planning engine 310 may suggest an optimal route(such as the first route) to be followed by the autonomous vehicle 110from the one or more routes based on the overall autonomy level of eachroute, the real-time traffic conditions of each route, the real-timeenvironmental conditions of each route, the trip time associated witheach route, or the route preferences of the user 112 associated with theautonomous vehicle 110. The ride planning engine 310 may be realized byutilizing one or more mathematical models, statistical models, and/oralgorithms. The ride planning engine 310 may be implemented by one ormore processors, such as, but are not limited to, an ASIC processor, aRISC processor, a CISC processor, and an FPGA.

The notification engine 312 may include suitable logic, circuitry,interfaces, and/or code, executable by the circuitry, that may beconfigured to perform one or more notification operations. For example,the notification engine 312 may present the one or more notifications,instructions, or commands on the navigation device 108 for assisting theuser 112 to drive the autonomous vehicle 110. The notification engine312 may communicate the one or more notifications, instructions, orcommands to the navigation device 108 for alerting the user 112 to apotential change in vehicle settings of the autonomous vehicle 110corresponding to the change in autonomy level of the route segment whileswitching between the route segments. The notification engine 312 may beimplemented by one or more processors, such as, but not limited to, anASIC processor, a RISC processor, a CISC processor, and an FPGA.

The transceiver 314 may include suitable logic, circuitry, interfaces,and/or code, executable by the circuitry, that may be configured totransmit (or receive) data to (or from) various servers or devices, suchas the map server 102, the database server 106, or the navigation device108. Examples of the transceiver 314 may include, but are not limitedto, an antenna, a radio frequency transceiver, a wireless transceiver,and a Bluetooth transceiver. The transceiver 314 may be configured tocommunicate with the map server 102, the database server 106, or thenavigation device 108 using various wired and wireless communicationprotocols, such as TCP/IP, UDP, LTE communication protocols, or anycombination thereof.

FIG. 4 is a block diagram that illustrates the database server 106, inaccordance with an exemplary embodiment of the disclosure. The databaseserver 106 includes circuitry such as a processor 402, a memory 404, atraffic database 406, an autonomy database 408, a weather database 410,a route database 412, and a transceiver 414 that communicate with eachother via a third communication bus (not shown).

The processor 402 may include suitable logic, circuitry, interfaces,and/or code, executable by the circuitry, that may be configured toperform one or more operations to store information pertaining tomultiple factors. Examples of the processor 402 may include, but are notlimited to an ASIC processor, a RISC processor, a CISC processor, and anFPGA. It will be apparent to a person of ordinary skill in the art thatthe processor 402 is compatible with multiple operating systems.

In an exemplary embodiment, the processor 402 may be configured tocontrol and manage extraction of requisite traffic information from athird-party traffic server, and store the traffic information in thetraffic database 406. The processor 402 may further control and manageextraction of requisite autonomy information from one or more autonomousvehicles (such as the autonomous vehicle 110, the autonomous vehicles114, and the autonomous vehicles 116), and store the autonomyinformation in the autonomy database 408. The processor 402 may furthercontrol and manage extraction of requisite weather information from athird-party weather server, and store the weather information in theweather database 410. The processor 402 may further control and manageextraction of requisite route information associated with the one ormore routes from a third-party route server, and store the routeinformation in the route database 412.

The memory 404 may include suitable logic, circuitry, interfaces, and/orcode, executable by the circuitry, that may be configured to store oneor more instructions that are executed by the processor 402 to extractrequisite information from the third-party traffic server, the one ormore autonomous vehicles, the third-party weather server, or thethird-party route server. Examples of the memory 404 may include, butare not limited to, a RAM, a ROM, a PROM, and an EPROM.

The traffic database 406 may include suitable logic, circuitry,interfaces, and/or code, executable by the circuitry, that may beconfigured to perform one or more database operations. For example, thetraffic database 406 may be configured to manage and store local trafficdata associated with the one or more route segments of the one or moreroutes of a geographical region. The local traffic data may includereal-time traffic flow conditions associated with the one or more routesegments of the one or more routes. For example, the local traffic datamay include traffic congestion-related information associated with eachroute segment. The traffic database 406 may further manage and storeinformation related to one or more temporal events (such as sportingevents, parades, and the like) that may cause significant impact uponthe traffic flow conditions. The traffic database 406 may also manageand store the obstruction information associated with each route segmentof the one or more routes. The obstruction information may includeinformation associated with road blockages, accidents, potholes, or thelike.

The autonomy database 408 may include suitable logic, circuitry,interfaces, and/or code, executable by the circuitry, that may beconfigured to perform one or more database operations. For example, theautonomy database 408 may be configured to manage and store the autonomyinformation pertaining to the one or more routes, such as the historicalautonomy levels associated with the one or more route segments of theone or more routes. In an embodiment, the autonomy database 408 mayinclude a tabular data structure including one or more rows and columnsfor storing the autonomy level information in a structured manner. Forexample, each row may be associated with a time or a time duration of aday, and the one or more columns may correspond to the number ofautonomous vehicles along each route segment, the autonomy level of eachautonomous vehicle, the autonomy level of each route segment based onthe autonomy level of each autonomous vehicle, or the like. In anembodiment, the autonomy database 408 may manage and store the real-timeautonomy levels of the one or more autonomous vehicles such as theautonomous vehicles 116.

The weather database 410 may include suitable logic, circuitry,interfaces, and/or code, executable by the circuitry, that may beconfigured to perform one or more database operations. For example, theweather database 410 may be configured to manage and store the weatherinformation associated with the one or more route segments of the one ormore routes. The weather information may include actual measured weathervariables in a local geographical region such as temperature, fog,light, humidity, or pollution levels associated with the one or moreroute segments. The weather database 410 may store weatherforecast-related information that may significantly affect the trafficflow along the one or more route segments of the one or more routes.

The route database 412 may include suitable logic, circuitry,interfaces, and/or code, executable by the circuitry, that may beconfigured to perform one or more database operations. For example, theroute database 412 may manage and store route information associatedwith the one or more route segments of the one or more routes. The routeinformation may include at least one of a road name of each roadassociated with each route segment, a road type of each road included inthe route, speed restrictions associated with each route segment, or theobstruction information associated with each route segment. The routeinformation may also include information associated with at least one ofthe shopping malls, the fuel centers, promotional events, and thetourist spots along the one or more route segments of each route.

The transceiver 414 may include suitable logic, circuitry, interfaces,and/or code, executable by the circuitry, that may be configured totransmit (or receive) data to (or from) various servers or devices, suchas the map server 102, the transportation server 104, or the navigationdevice 108. Examples of the transceiver 414 may include, but are notlimited to, an antenna, a radio frequency transceiver, a wirelesstransceiver, and a Bluetooth transceiver. The transceiver 414 may beconfigured to communicate with the map server 102, the transportationserver 104, or the navigation device 108 using various wired andwireless communication protocols, such as TCP/IP, UDP, LTE communicationprotocols, or any combination thereof.

FIGS. 5A and 5B are diagrams that collectively illustrate the digitalautonomy map rendered on the navigation device 108 of the autonomousvehicle 110, in accordance with an exemplary embodiment of thedisclosure. The map server 102 may be configured to render a userinterface 502 on the display of the navigation device 108 based on thenavigation request. The user interface 502 may present the digitalautonomy map generated based on the navigation request. The digitalautonomy map may include the one or more route segments of at least thefirst route connecting the source location and the destination location.Further, each route segment on the digital autonomy map may be taggedwith the autonomy level that has been determined based on the extractedhistorical autonomy levels. The historical autonomy levels aregenerated, by the transportation server 104 prior to the extraction,based on the historical weather information and the historicalconfiguration of the one or more sensors and processing components ofthe autonomous vehicles 114. The digital autonomy map presented on theuser interface 502 may be further updated in real-time based on at leastone of the first sensor data, the second sensor data, the real-timeroute information, the real-time weather information, and the real-timetraffic information.

In an exemplary scenario, the user interface 502 may present a digitalautonomy map of a geographical region as shown in FIG. 5A. The digitalautonomy map may correspond to a satellite image of the geographicalregion including the one or more route segments (such as segments S₁ andS₂) of the one or more routes. Each of the one or more route segments ofthe one or more routes are tagged with an autonomy level by the mapserver 102 based on at least the historical autonomy levels associatedwith each route segment. For example, the segment S₁ is tagged with anautonomy level “1” (i.e., LVL 1 as shown in FIG. 5A) and the segment S₂is tagged with an autonomy level “3” (i.e., LVL 3 as shown in FIG. 5A).One or more autonomous vehicles (such as the autonomous vehicle 110) mayutilize the digital autonomy map to traverse from one location toanother location via the one or more route segments. When the one ormore autonomous vehicles are traversing along the one or more routesegments, the autonomy level of each route segment may be dynamicallyupdated based on at least one of the sensor data (such as the firstsensor data and the second sensor data) of the one or more autonomousvehicles, the real-time traffic information, the real-time routeinformation, and the real-time weather information associated with eachroute. For example, an autonomous vehicle V₁ is traversing from a sourcelocation D₁ to a destination location D₂ via the segments S₁ and S₂ asshown in FIG. 5A. When the autonomous vehicle V₁ is traversing along thesegment S₂, the map server 102 updates the digital autonomy map byupdating at least the autonomy level of the segment S₂ in real-time fromlevel “3” (i.e., LVL 3 as shown in FIG. 5A) to level “2” (i.e., LVL 2 asshown in FIG. 5B). The updated digital autonomy map may be utilized by adriver (such as the user 112) or the autonomous vehicle V₁ (such as theautonomous vehicle 110) for controlling and managing the various transitand vehicle dynamics operations along the segment S₂ to reach thedestination location D₂.

FIGS. 6A and 6B are diagrams that collectively illustrate a navigationinterface 602 rendered on the navigation device 108, in accordance withan exemplary embodiment of the disclosure. The map server 102 may beconfigured to render the navigation interface 602 on the display of thenavigation device 108 of the autonomous vehicle 110, when the navigationdevice 108 is used for navigating from the source location to thedestination location.

As shown in FIG. 6A, the navigation engine 210 renders the navigationinterface 602 on the display of the navigation device 108 forfacilitating the navigation of the autonomous vehicle 110 along the oneor more route segments of the first route. The navigation interface 602may present a satellite image of the geographical region including theone or more route segments of at least the first route that isconnecting the source location with the destination location. Each routesegment is tagged with the determined autonomy level. The navigationinterface 602 may further present the one or more navigationinstructions and commands rendered by the navigation engine 210 forproviding the real-time driving assistance to the autonomous vehicle110. The one or more navigation instructions and commands may includeroad-by-road directions, and/or turn-by-turn directions for navigatingalong the first route. The navigation interface 602 may further presentthe route information associated with the one or more route segments ofthe first route. The route information may include at least one of theroad name of each road associated with each route segment, the road typeof each road included in the first route, the speed restrictionsassociated with each route segment, or the obstruction informationassociated with each route segment.

As shown in FIG. 6B, the navigation interface 602 may present the one ormore notifications, instructions, or commands that are rendered on thenavigation interface 602 (or a separate notification interface) duringthe ride. In one example, the navigation engine 210 may render the oneor more notifications, instructions, or commands. In another example,the notification engine 312 may render the one or more notifications,instructions, or commands. For example, as shown in FIG. 6B, thenotification engine 312 renders a first notification 604 on thenavigation interface 602. The first notification 604 may be renderedwhen the autonomous vehicle 110 is nearing a route segment switchingfrom one route segment (such as a segment S₁ having an autonomy level“3”) to another route segment (such as a segment S₂ having an autonomylevel “2”). The first notification 604 may include the autonomy level ofthe approaching route segment (such as the segment S₂) and one or moreinstructions or commands to assist the autonomous vehicle 110 or theuser 112 during the route segment switching based on the autonomy levelof the approaching route segment. The navigation interface 602 mayfurther present an estimated time duration and/or distance associatedwith the route segment switching. In an exemplary scenario, the firstnotification 604 may be displayed as “Dear Driver, Autonomy level of thevehicle switching from level 3 to level 2 in 8:00 minutes. Please be incontrol of the steering wheel”, as shown in FIG. 6B.

FIGS. 7A and 7B, collectively, illustrate a flow chart 700 of a methodfor generating the digital autonomy map for providing driving assistanceto the autonomous vehicle 110, in accordance with an exemplaryembodiment of the disclosure.

At 702, the historical autonomy levels associated with the one or moreroute segments of at least the first route are extracted. In anembodiment, the map server 102 may be configured to extract thehistorical autonomy levels from the autonomy database 408. Thehistorical autonomy levels may be extracted based on at least one of thetime or the time duration of the day associated with the autonomousvehicle 110. Prior to the extracting of the historical autonomy levels,the historical autonomy levels may be generated, by the transportationserver 104, based on at least the historical configuration of the one ormore sensors and processing components of the autonomous vehicles 114and the historical weather information associated with the one or moreroute segments of the first route. The historical autonomy levels may befurther generated based on the historical driving conditions of the oneor more route segments of the first route.

At 704, the autonomy level for each route segment is determined based onthe extracted historical autonomy levels. In an embodiment, the mapserver 102 may be configured to determine the autonomy level for eachroute segment.

At 706, the digital autonomy map including the one or more routesegments of the first route is generated. In an embodiment, the mapserver 102 may be configured to generate the digital autonomy map. Eachroute segment on the digital autonomy map may be tagged with therespective determined autonomy level. The digital autonomy map may beutilized by the autonomous vehicle 110 for controlling and managing thetransit operations between the source location and the destinationlocation. In one example, the digital autonomy map may be different fordifferent categories of autonomous vehicles and/or differentconfiguration of the sensors and processing components. In anotherexample, the digital autonomy map may be the same for the samecategories of autonomous vehicles and/or the same configuration of thesensors and processing components.

At 708, the real-time autonomy levels are received. In an embodiment,the map server 102 may be configured to receive the real-time autonomylevels from the autonomous vehicles 116. The autonomous vehicles 116 maybe currently traversing the one or more route segments of the firstroute. Further, the autonomous vehicles 116 may be traversing ahead ofthe autonomous vehicle 110.

At 710, the first sensor data is received. In an embodiment, the mapserver 102 may be configured to receive the first sensor data from theautonomous vehicle 110. The first sensor data may include at least oneof the GPS data, the image data of the exterior environment, the RADARdata, the ultrasonic data, and the LiDAR data associated with theautonomous vehicle 110.

At 712, the second sensor data is received. In an embodiment, the mapserver 102 may be configured to receive the second sensor data from theautonomous vehicles 116. The second sensor data may include at least oneof the GPS data, the image data of the exterior environment, the RADARdata, the ultrasonic data, and the LiDAR data associated with theautonomous vehicles 116.

At 714, the real-time traffic information, the real-time routeinformation, and the real-time weather information are retrieved. In anembodiment, the map server 102 may be configured to retrieve thereal-time traffic information, the real-time route information, and thereal-time weather information from the database server 106. For example,the map server 102 may retrieve the real-time traffic information of thefirst route from the traffic database 406, the real-time weatherinformation of the first route from the weather database 410, and thereal-time route information of the first route from the route database412. The real-time route information may include at least the routesegment type, the speed restriction information, and the obstructioninformation of each route segment. The real-time traffic information mayinclude the real-time traffic conditions associated with each routesegment. The real-time weather information may include at least thereal-time temperature, fog, light, humidity, and pollution informationassociated with each route segment.

At 716, the autonomy level of each route segment is dynamically updated.In an embodiment, the map server 102 may be configured to dynamicallyupdate the autonomy level of each route segment of the first route. Theautonomy level of each route segment may be dynamically updated based onat least one of the real-time autonomy levels, the first sensor data,the second sensor data, the real-time traffic information, the real-timeroute information, and the real-time weather information. The autonomylevel of each route segment may be further updated based on the vehiclecategory of at least one of the autonomous vehicle 110 and theautonomous vehicles 116. The autonomy level of each route segment may befurther updated based on the operating state of the one or more sensorsand processing components of at least one of the autonomous vehicle 110and the autonomous vehicles 116.

At 718, the digital autonomy map is rendered on the navigation device108. In an embodiment, the map server 102 may be configured to renderthe digital autonomy map (such as the first time generated digitalautonomy map or the updated digital autonomy map) on the navigationdevice 108 of the autonomous vehicle 110. The transit operations of theautonomous vehicle 110 between the source location and the destinationlocation may be controlled and managed based on at least the navigationand autonomy information indicated by the rendered digital autonomy map.

FIG. 8 is a block diagram that illustrates a system architecture of acomputer system 800 for generating the digital autonomy map for theautonomous vehicle 110 for providing the real-time driving assistance tothe autonomous vehicle 110, in accordance with an exemplary embodimentof the disclosure. An embodiment of the disclosure, or portions thereof,may be implemented as computer readable code on the computer system 800.In one example, the map server 102, the transportation server 104,and/or the database server 106 of FIG. 1 may be implemented in thecomputer system 800 using hardware, software, firmware, non-transitorycomputer readable media having instructions stored thereon, or acombination thereof and may be implemented in one or more computersystems or other processing systems. Hardware, software, or anycombination thereof may embody modules and components used to implementthe driving assistance method of FIGS. 7A-7B.

The computer system 800 may include a processor 802 that may be aspecial purpose or a general-purpose processing device. The processor802 may be a single processor, multiple processors, or combinationsthereof. The processor 802 may have one or more processor “cores.”Further, the processor 802 may be coupled to a communicationinfrastructure 804, such as a bus, a bridge, a message queue, thecommunication network 118, multi-core message-passing scheme, and thelike. The computer system 800 may further include a main memory 806 anda secondary memory 808. Examples of the main memory 806 may include RAM,ROM, and the like. The secondary memory 808 may include a hard diskdrive or a removable storage drive (not shown), such as a floppy diskdrive, a magnetic tape drive, a compact disc, an optical disk drive, aflash memory, or the like. Further, the removable storage drive may readfrom and/or write to a removable storage device in a manner known in theart. In an embodiment, the removable storage unit may be anon-transitory computer readable recording media.

The computer system 800 may further include an I/O port 810 and acommunication interface 812. The I/O port 810 may include various inputand output devices that are configured to communicate with the processor802. Examples of the input devices may include a keyboard, a mouse, ajoystick, a touchscreen, a microphone, and the like. Examples of theoutput devices may include a display screen, a speaker, headphones, andthe like. The communication interface 812 may be configured to allowdata to be transferred between the computer system 800 and variousdevices that are communicatively coupled to the computer system 800.Examples of the communication interface 812 may include a modem, anetwork interface, i.e., an Ethernet card, a communications port, andthe like. Data transferred via the communication interface 812 may besignals, such as electronic, electromagnetic, optical, or other signalsas will be apparent to a person of ordinary skill in the art. Thesignals may travel via a communications channel, such as thecommunication network 118, which may be configured to transmit thesignals to the various devices that are communicatively coupled to thecomputer system 800. Examples of the communication channel may include awired, wireless, and/or optical medium such as cable, fiber optics, aphone line, a cellular phone link, a radio frequency link, and the like.The main memory 806 and the secondary memory 808 may refer tonon-transitory computer readable mediums that may provide data thatenables the computer system 800 to implement the driving assistancemethod illustrated in FIGS. 7A-7B.

Various embodiments of the disclosure provide the map server 102 forgenerating the digital autonomy map for the autonomous vehicle 110 forproviding the real-time driving assistance to the autonomous vehicle110. The map server 102 may be configured to extract the historicalautonomy levels associated with the one or more route segments of atleast one route (such as the first route) connecting the source locationand the destination location of the autonomous vehicle 110. The mapserver 102 may be further configured to determine the autonomy level foreach route segment based on at least the extracted historical autonomylevels. The map server 102 may be further configured to generate thedigital autonomy map including the one or more route segments of thefirst route. Each route segment on the digital autonomy map is taggedwith the autonomy level. The generated digital autonomy map may beutilized by the autonomous vehicle 110 to control the transit operationsbetween the source location and the destination location. The map server102 may be further configured to receive the real-time autonomy levelsfrom the autonomous vehicles 116 that may be currently traversing theone or more route segments of the first route. The autonomous vehicles116 may be traversing ahead of the autonomous vehicle 110. The mapserver 102 may be further configured to dynamically update the autonomylevel of each route segment on the digital autonomy map based on thereceived real-time autonomy levels to provide the real-time drivingassistance to the autonomous vehicle 110.

Various embodiments of the disclosure provide a non-transitory computerreadable medium having stored thereon, computer executable instructions,which when executed by a computer, cause the computer to executeoperations for the map server 102 for generating the digital autonomymap for the autonomous vehicle 110 for providing the real-time drivingassistance to the autonomous vehicle 110. The operations includeextracting, by the map server 102 from the database server 106, thehistorical autonomy levels associated with the one or more routesegments of at least one route (such as the first route) connecting thesource location and the destination location of the autonomous vehicle110. The operations further include determining, by the map server 102,the autonomy level for each route segment based on at least theextracted historical autonomy levels. The operations further includegenerating, by the map server 102, the digital autonomy map includingthe one or more route segments of the at least one route. Each routesegment on the digital autonomy map may be tagged with the autonomylevel. The digital autonomy map may be utilized by the autonomousvehicle 110 for controlling the transit operations between the sourcelocation and the destination location. The operations further includereceiving, by the map server 102, the real-time autonomy levels from theautonomous vehicles 116 that are currently traversing the one or moreroute segments of the at least one route. The autonomous vehicles 116may be traversing the one or more route segments ahead of the autonomousvehicle 110. The operations further include dynamically updating, by themap server 102, the autonomy level of each route segment of the at leastone route on the digital autonomy map based on the received real-timeautonomy levels for providing driving assistance to the autonomousvehicle 110.

Various embodiments of the disclosure provide a non-transitory computerreadable medium having stored thereon, computer executable instructions,which when executed by a computer, cause the computer to executeoperations for generating the digital autonomy map for the autonomousvehicle 110 for providing the real-time driving assistance to theautonomous vehicle 110. The operations include selecting, by thetransportation server 104, at least one route (such as the first route)from the one or more routes. The at least one route connects the sourcelocation and the destination location of the autonomous vehicle 110. Theat least one route may be selected from the one or more routes based onat least one of the overall autonomy level of each route, the real-timetraffic conditions of each route, the real-time environmental conditionsof each route, the trip time associated with each route, and the routepreferences of the user 112 associated with the autonomous vehicle 110.The user 112 may be at least one of a driver or a passenger associatedwith the autonomous vehicle 110. The driver may be selected from a setof available drivers based on at least the autonomy level of each routesegment of the at least one route. The operations further includegenerating, by the transportation server 104, the historical autonomylevels associated with the one or more route segments of the at leastone route. The operations further include determining, by the map server102, the autonomy level for each route segment based on at least thegenerated historical autonomy levels. The operations further includegenerating, by the map server 102, the digital autonomy map includingthe one or more route segments of the at least one route. Each routesegment on the digital autonomy map may be tagged with the autonomylevel. The digital autonomy map may be utilized by the autonomousvehicle 110 for controlling the transit operations between the sourcelocation and the destination location. The operations further includereceiving, by the map server 102, the real-time autonomy levels from theautonomous vehicles 116 that are currently traversing the one or moreroute segments of the at least one route. The autonomous vehicles 116may be traversing the one or more route segments ahead of the autonomousvehicle 110. The operations further include dynamically updating, by themap server 102, the autonomy level of each route segment of the at leastone route on the digital autonomy map based on the received real-timeautonomy levels for providing driving assistance to the autonomousvehicle 110.

The disclosed embodiments encompass numerous advantages. Exemplaryadvantages of the method and the system include generating the digitalautonomy map for an autonomous vehicle such as the autonomous vehicle110. The digital autonomy map may be utilized, by a transport serviceprovider (e.g., a cab service provider such as OLA), to control andmanage the transit operations of the autonomous vehicles from onelocation to another location in an effective and efficient manner. Forexample, for deployment of the autonomous vehicle 110 for a trip by atransport service provider, various factors such as conditions of theselected route for the trip, weather conditions associated with theselected route, health of the autonomous vehicle 110, traffic conditionsassociated with the selected route, necessity of a driver for drivingthe autonomous vehicle 110, the autonomy levels of various routesegments of the selected route, and the like, may be taken intoconsideration for ensuring a hassle free ride that is safe for theautonomous vehicle 110 and the user 112 (such as the driver or thepassenger). The deployment of the autonomous vehicle 110 along with thedriver is not always feasible considering an overall increased cost ofthe trip that may not be desirable. Thus, the need of the driver for theautonomous vehicle 110 may be determined based on the autonomy levels ofthe various route segments of the selected route. Further, for efficientcomputation of the autonomy levels, various internal and externalfactors are taken into consideration for dynamically updating thedigital autonomy map in real-time. Further, the digital autonomy map maybe utilized to ensure that the driver is well aware of the route segmentswitching by communicating various notifications, instructions, orcommands. Thus, the generation of the digital autonomy map and thedynamic updating of the generated digital autonomy map after a regularinterval of time may facilitate the efficient and effective planning forthe trip prior to the deployment of the autonomous vehicle 110. Suchefficient and effective planning for the trip may assist the transportservice provider to optimize utilization of available resources, improveuser comfort and safety, and thereby improve cost effectiveness of thetrip by reducing vehicle maintenance and driver costs, thereby enhancingthe efficiency of the trip.

A person of ordinary skill in the art will appreciate that embodimentsand exemplary scenarios of the disclosed subject matter may be practicedwith various computer system configurations, including multi-coremultiprocessor systems, minicomputers, mainframe computers, computerslinked or clustered with distributed functions, as well as pervasive orminiature computers that may be embedded into virtually any device.Further, the operations may be described as a sequential process,however some of the operations may in fact be performed in parallel,concurrently, and/or in a distributed environment, and with program codestored locally or remotely for access by single or multiprocessormachines. In addition, in some embodiments, the order of operations maybe rearranged without departing from the spirit of the disclosed subjectmatter.

Techniques consistent with the disclosure provide, among other features,systems and methods for providing driving assistance to variousautonomous vehicles. While various exemplary embodiments of thedisclosed systems and methods have been described above, it should beunderstood that they have been presented for purposes of example only,and not limitations. It is not exhaustive and does not limit thedisclosure to the precise form disclosed. Modifications and variationsare possible in light of the above teachings or may be acquired frompracticing of the disclosure, without departing from the breadth orscope.

While various embodiments of the disclosure have been illustrated anddescribed, it will be clear that the disclosure is not limited to theseembodiments only. Numerous modifications, changes, variations,substitutions, and equivalents will be apparent to those skilled in theart, without departing from the spirit and scope of the disclosure, asdescribed in the claims.

What is claimed is:
 1. A driving assistance method, comprising:extracting, by a map server from a database server, historical autonomylevels associated with one or more route segments of at least one routeconnecting a source location and a destination location of a firstautonomous vehicle; determining, by the map server, an autonomy levelfor each route segment based on at least the extracted historicalautonomy levels; generating, by the map server, a digital autonomy mapincluding the one or more route segments of the at least one route,wherein each route segment on the digital autonomy map is tagged withthe autonomy level, and wherein the digital autonomy map is utilized bythe first autonomous vehicle for controlling transit operations betweenthe source location and the destination location; receiving, by the mapserver, real-time autonomy levels from one or more second autonomousvehicles that are currently traversing the one or more route segments ofthe at least one route, wherein the one or more second autonomousvehicles are traversing the one or more route segments ahead of thefirst autonomous vehicle; and dynamically updating, by the map server,the autonomy level of each route segment of the at least one route onthe digital autonomy map based on the received real-time autonomy levelsfor providing driving assistance to the first autonomous vehicle.
 2. Thedriving assistance method of claim 1, wherein, prior to the extractingof the historical autonomy levels, the historical autonomy levels aregenerated based on historical driving conditions of the one or moreroute segments of the at least one route and historical configuration ofone or more sensors and processing components of one or more thirdautonomous vehicles.
 3. The driving assistance method of claim 2,wherein the historical autonomy levels are extracted based on a time ora time duration of a day associated with the first autonomous vehicle.4. The driving assistance method of claim 1, further comprisingreceiving, by the map server, first sensor data from the firstautonomous vehicle and second sensor data from the one or more secondautonomous vehicles, wherein the first sensor data and the second sensordata include at least global positioning system (GPS) data, image dataof an exterior environment, radio detection and ranging (RADAR) data,ultrasonic data, and light detection and ranging (LiDAR) data.
 5. Thedriving assistance method of claim 4, further comprising retrieving, bythe map server, at least real-time route information of the at least oneroute from a route database, real-time traffic information of the atleast one route from a traffic database, and real-time weatherinformation of the at least one route from a weather database.
 6. Thedriving assistance method of claim 5, wherein the real-time routeinformation includes at least a route segment type, speed restrictioninformation, and obstruction information of each route segment of the atleast one route, the real-time traffic information includes real-timetraffic conditions associated with each route segment of the at leastone route, and the real-time weather information includes at leastreal-time temperature, fog, light, humidity, and pollution informationassociated with each route segment of the at least one route.
 7. Thedriving assistance method of claim 5, wherein the autonomy level of eachroute segment of the at least one route is further dynamically updatedon the digital autonomy map based on at least one of the first sensordata, the second sensor data, the real-time route information, thereal-time traffic information, and the real-time weather information. 8.The driving assistance method of claim 7, wherein the autonomy level ofeach route segment of the at least one route is further dynamicallyupdated based on at least: a vehicle category of at least one of thefirst autonomous vehicle and the one or more second autonomous vehicles,and an operating state of one or more sensors and processing componentsof at least one of the first autonomous vehicle and the one or moresecond autonomous vehicles.
 9. The driving assistance method of claim 1,further comprising rendering, by the map server, the digital autonomymap on a navigation device of the first autonomous vehicle, wherein thetransit operations between the source location and the destinationlocation are controlled based on at least navigation and autonomyinformation indicated by the rendered digital autonomy map.
 10. Adriving assistance system, comprising: a map server comprising circuitryconfigured to: extract, from a database server, historical autonomylevels associated with one or more route segments of at least one routethat connects a source location and a destination location of a firstautonomous vehicle; determine an autonomy level for each route segmentbased on at least the extracted historical autonomy levels; generate adigital autonomy map including the one or more route segments of the atleast one route, wherein each route segment on the digital autonomy mapis tagged with the autonomy level, and wherein the digital autonomy mapis utilized by the first autonomous vehicle to control transitoperations between the source location and the destination location;receive real-time autonomy levels from one or more second autonomousvehicles that currently traverse the one or more route segments of theat least one route, wherein the one or more second autonomous vehiclestraverse the one or more route segments ahead of the first autonomousvehicle; and dynamically update the autonomy level of each route segmentof the at least one route on the digital autonomy map based on thereceived real-time autonomy levels to provide driving assistance to thefirst autonomous vehicle.
 11. The driving assistance system of claim 10,wherein the circuitry is further configured to generate, prior to theextraction of the historical autonomy levels, the historical autonomylevels based on historical driving conditions of the one or more routesegments of the at least one route and historical configuration of oneor more sensors and processing components of one or more thirdautonomous vehicles.
 12. The driving assistance system of claim 11,wherein the circuitry is further configured to extract the historicalautonomy levels based on a time or a time duration of a day associatedwith the first autonomous vehicle.
 13. The driving assistance system ofclaim 10, wherein the circuitry is further configured to receive firstsensor data from the first autonomous vehicle and second sensor datafrom the one or more second autonomous vehicles, and wherein the firstsensor data and the second sensor data include at least globalpositioning system (GPS) data, image data of an exterior environment,radio detection and ranging (RADAR) data, ultrasonic data, and lightdetection and ranging (LiDAR) data.
 14. The driving assistance system ofclaim 13, wherein the circuitry is further configured to retrieve atleast real-time route information of the at least one route from a routedatabase, real-time traffic information of the at least one route from atraffic database, and real-time weather information of the at least oneroute from a weather database.
 15. The driving assistance system ofclaim 14, wherein the real-time route information includes at least aroute segment type, speed restriction information, and obstructioninformation of each route segment of the at least one route, thereal-time traffic information includes real-time traffic conditionsassociated with each route segment of the at least one route, and thereal-time weather information includes at least real-time temperature,fog, light, humidity, and pollution information associated with eachroute segment of the at least one route.
 16. The driving assistancesystem of claim 14, wherein the circuitry is further configured todynamically update the autonomy level of each route segment of the atleast one route on the digital autonomy map based on at least one of thefirst sensor data, the second sensor data, the real-time routeinformation, the real-time traffic information, and the real-timeweather information.
 17. The driving assistance system of claim 16,wherein the circuitry is further configured to dynamically update theautonomy level of each route segment of the at least one route on thedigital autonomy map based on at least: a vehicle category of at leastone of the first autonomous vehicle and the one or more secondautonomous vehicles, and an operating state of one or more sensors andprocessing components of at least one of the first autonomous vehicleand the one or more second autonomous vehicles.
 18. The drivingassistance system of claim 10, wherein the circuitry is furtherconfigured to render the digital autonomy map on a navigation device ofthe first autonomous vehicle, and wherein the transit operations betweenthe source location and the destination location are controlled based onat least navigation and autonomy information indicated by the rendereddigital autonomy map.
 19. A driving assistance system, comprising: atransportation server comprising circuitry configured to: select atleast one route from one or more routes, wherein the at least one routeconnects a source location and a destination location of a firstautonomous vehicle; and generate historical autonomy levels associatedwith one or more route segments of the at least one route; and a mapserver comprising circuitry configured to: determine an autonomy levelfor each route segment based on at least the generated historicalautonomy levels; generate a digital autonomy map including the one ormore route segments of the at least one route, wherein each routesegment on the digital autonomy map is tagged with the autonomy level,and wherein the digital autonomy map is utilized by the first autonomousvehicle to control transit operations between the source location andthe destination location; receive real-time autonomy levels from one ormore second autonomous vehicles that currently traverse the one or moreroute segments of the at least one route, wherein the one or more secondautonomous vehicles traverse the one or more route segments ahead of thefirst autonomous vehicle; and dynamically update the autonomy level ofeach route segment of the at least one route on the digital autonomy mapbased on the received real-time autonomy levels to provide drivingassistance to the first autonomous vehicle.
 20. The system of claim 19,wherein circuitry of the transportation server is further configured toselect the at least one route from the one or more routes based on atleast an overall autonomy level of each route, real-time trafficconditions of each route, real-time environmental conditions of eachroute, a trip time associated with each route, and route preferences ofa user associated with the first autonomous vehicle, wherein the user isat least one of a driver or a passenger associated with the firstautonomous vehicle, and wherein the driver is selected from a set ofavailable drivers based on at least the autonomy level of each routesegment of the at least one route.